Score-Based Diffusion Models for Bayesian Image Reconstruction

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Michael T. McCann, Hyungjin Chung, Jong Chul Ye, Marc Louis Klasky

Published: 2023, Last Modified: 05 Mar 2026ICIP 2023EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This paper explores the use of score-based diffusion models for Bayesian image reconstruction. Diffusion models are an efficient tool for generative modeling. Diffusion models can also be used for solving image reconstruction problems. We present a simple and flexible algorithm for training a diffusion model and using it for maximum a posteriori reconstruction, minimum mean square error reconstruction, and posterior sampling. We present experiments on both a linear and a nonlinear reconstruction problem that highlight the strengths and limitations of the approach.